Next-generation sequencing (NGS) technology has provided a powerful tool to produce a gigantic amount of biological data that will shed light on path towards personalized medicine. While the cost of high throughput genome sequencing is decreasing in terms of merely acquiring sequence data, the analysis and interpretation of these large-scale sequencing data remains to pose a major challenge. To call variants from NGS data, many aligners and variant callers have been developed and composed into diverse pipelines. A typical pipeline contains an aligner and a variant caller: the former maps the sequencing reads to a reference genome, and the latter identifies variant sites and assigns a genotype to the subjects. In going through the pipeline, users often need to set many parameters in order to properly analyze the sequencing data. Importantly, some parameters need to be optimized for accurately calling the variant, e.g., on the basis of the type of cells or the ethnic groups from which the sample is prepared. In particular, the optimal value for a parameter may depend on the location in the genome. For example, the parameters associated with the likelihood of a variant occurrence may depend on the location in the genome. However, due to the enormous computation required for each run of the pipeline, going through the entire variant call pipeline to test the optimal value of the parameter for each location in the genome is practically infeasible. Therefore, there is continuing need to develop new methods and systems to optimize parameter settings on the basis of the location in the genome for analyzing NGS data.